Dynamic metabolism monitoring system

ABSTRACT

A metabolism monitoring system, data analyzer, and method is disclosed for processing data to very quickly estimate metabolic parameters. The analyzer of the invention uses a dynamic observer state estimator to estimate the parameters and tracks their values as they change with alterations in the patient metabolism or testing conditions. The state estimator does not use the conventional steady state solutions. The observer predicts the patient consumption of oxygen and exhalation of carbon dioxide at a subsequent point in time, corrects these values using measurements of air flow rate and chamber levels of oxygen and carbon dioxide taken at that subsequent time, and repeats for the next time. The corrected values are used for estimation of the metabolic parameters.

RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application Ser.No. 60/719,883 filed 23 Sep. 2005.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The present invention relates generally to devices for measuringmetabolic parameters. More particularly, the present invention relatesto apparatus and methods to generate rapid estimates of theseparameters.

2. General Background and State of the Art

Metabolic chambers based on open circuit spirometry are used todetermine the metabolic parameters of a patient within it. With theobesity epidemic of current concern, such information is of particularinterest for infants and young children as a noninvasive tool toevaluate their metabolic status and food requirements. Metabolicchambers are also used to monitor the metabolism of non-humans such asfarm animals (cattle, pigs, . . . ) and laboratory test animals (rats,mice, monkeys, . . . ). To be effective clinically, the chambers need toproduce metabolic parameters quickly and reliably.

A metabolic chamber can be a rigid structure made of solid plastic orglass walls, or a flexible structure like the plastic film canopy placedover patients. To generate the metabolic parameters, the chamber air issteadily removed while fresh air enters, and the removed air is sampledto determine the fractions of CO₂ and O₂ and the air removal flow rate.Metabolic parameters are determined by combining these in equationsdescribing the steady state condition of the chamber. These results arenot valid until steady state is achieved, and this is dependent on thevolume of the chamber and the entry and removal flow rates. In typicalchambers designed for infants, steady state can take as long as an hourand, since during this time the metabolism of the patient can bechanging, steady state may never actually exist

The ability to determine metabolic parameters quickly while the chamberis not at steady state is preferred and would make it possible to reducethe time the patient is in the chamber and to produce more accuratemetabolic parameter values. There is a need to provide an apparatus andmethod to accomplish this.

Commercially available metabolism monitoring devices may not enclose thepatient, but use a mouthpiece through which the patient breathes into amixing chamber or a small chamber enclosing only the head (Deltatrac IIby Datex-Ohmeda). Commercial devices (Deltatrac II) are also used tomonitor intensive care patients lying under large canopies havingvolumes large enough to be affected by steady state equilibration andthat can be improved using the invention described herein.

SUMMARY OF THE INVENTION

Accordingly, it is an object of the present invention to provide adynamic method to produce accurate metabolic parameters while thechamber is not in steady state.

An additional object of the invention is to provide a chamber designedto optimize the dynamic metabolic parameter determination method.

It is yet another object of the invention to retro-fit existing chamberssuch that they are made compatible with the dynamic metabolic parameterdetermination method.

These and other objectives are achieved by the present invention, which,in a broad aspect, is a dynamic method of analyzing the metabolicchamber measurements in a manner that is independent of the steady statechamber condition, and that produces accurate metabolic parameter valuesmany times faster than current steady state methods. The presentinvention also includes a chamber design to facilitate the dynamicmetabolic parameter determination. The present invention also includes adesign to retro-fit existing non-facilitated chambers.

Further objects and advantages of the present invention will become moreapparent from the following description of the preferred embodimentswhich, taken in conjunction with the accompanying drawings, illustrate,by way of example, the principles of the invention.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a schematic representation of a conventional metabolic chamberusing steady state methods to estimate metabolic parameters.

FIG. 2 is a flow diagram showing the stepwise operation of a dynamicmetabolic parameter estimator.

FIG. 3 is a schematic representation of a metabolic chamber usingdynamic methods to estimate metabolic parameters.

FIG. 4 is a schematic representation of a conventional metabolic chamberretrofitted with a dynamic metabolic parameter estimator.

FIG. 5 is a graph comparing the simulated responses of the conventionalsteady state metabolic parameter estimator with a dynamic estimatorhaving fixed gains where the patient metabolic rate changes.

FIG. 6 is a graph comparing the simulated responses of the conventionalsteady state metabolic parameter estimator with a dynamic estimatorhaving varying gains where the patient metabolic rate changes.

FIG. 7 is a graph comparing the simulated responses of the conventionalsteady state metabolic parameter estimator with a dynamic estimatorhaving fixed gains where the sample flow rate changes.

FIG. 8 is a graph comparing the simulated responses of the conventionalsteady state metabolic parameter estimator with a dynamic estimatorhaving fixed gains where the patient metabolic rate changes and wherethe measurement instruments have ten times the measurement noise.

FIG. 9 is a graph comparing the simulated responses of the conventionalsteady state metabolic parameter estimator with a dynamic estimatorhaving varying gains where the patient metabolic rate changes and wherethe measurement instruments have ten times the measurement noise.

FIG. 10 is a graph illustrating the simulated over-shoot responses ofthe dynamic estimator having fixed and varying gains where the chambervolume is specified incorrectly 20% to large.

FIG. 1 is a graph illustrating the simulated under-shoot responses ofthe dynamic estimator having fixed and varying gains where the chambervolume is specified incorrectly 20% too small.

DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS OF THE INVENTION

Current metabolism monitoring devices are based on a simplistic steadystate mathematical model of the chamber dynamics. Even small chambersenclosing infants can take as much as an hour to reach steady state, ifit exists at all given normal changes in the patient's metabolism withactivity. Means to eliminate this delay will greatly advance theclinical use of metabolic chambers.

FIG. 1 illustrates a conventional metabolic parameter estimation system1 using a steady state data analyzer 2. The chamber 3 encloses a patient4, a portion of a patient, or otherwise exchanges gas with a patient.The patient is a F_(CO2) _(—) _(patient) source 5 of carbon dioxide anda F_(O2) _(—) _(patient) sink 6 of oxygen. The gas 7 within the chamberis evacuated at rate F_(sample) 8 for measurement and replaced 9 byF_(ambient) inflow from ambient 10. The evacuated air 8 is analyzed 11to determine the volume fraction of oxygen and carbon dioxide,temperature, humidity, pressure, and the gas outflow rate 8 is measured15 by flow sensor 12. A fan or air pump 13 acts to move the air throughthe system.

The chamber 3 has volume V_(chamber) after compensation for the patient4 volume, and a fan (not shown) is included within the chamber to mixand uniformly distribute the chamber 3 gas contents 7. The data 14 fromthe gas analyzer 11 and the data 15 from the flow sensor 12 are allowedto reach a steady state (unchanging) equilibrium condition and then usedto calculate steady state metabolic parameters 16 based on estimates ofthe patient's oxygen consumption rate 6, carbon dioxide generation rate5, and gas outflow rate data 15.

Derivation of the Chamber Dynamic Mathematical Model

While the steady state chamber analyzer 2 is based on a steady statemodel of the chamber, the non-steady state dynamic chamber analyzer isbased on a dynamic chamber model. The gas flow dynamics reflect themovement of oxygen, carbon dioxide and metabolically inert gases fromambient air through the chamber where it exchanges with the patient, andpasses through a gas analyzer drawn by a fan or air pump. The gasanalyzer determines the volumetric fractions of oxygen and carbondioxide along with the pressure, temperature, flow rate, and humidity ofthe sampled chamber air. Temperature, pressure and humidity are used tocorrect the measured pressure to standard conditions.

To begin the mathematical model, the molar content of air within thechamber is described based on the flow in, flow out, and that providedby the patient as{dot over (q)} _(O2) _(—) _(chamber) ={dot over (q)} _(O2) _(—)_(ambient) −{dot over (q)} _(O2) _(—) _(sample) −{dot over (q)} _(O2)_(—) _(patient){dot over (q)} _(CO2) _(—) _(chamber) ={dot over (q)} _(CO2) _(—)_(ambient) −{dot over (q)} _(O2) _(—) _(sample) −{dot over (q)} _(CO2)_(—) _(patient){dot over (q)} _(N2) _(—) _(chamber) ={dot over (q)} _(N2) _(—)_(ambient) −{dot over (q)} _(N2) _(—) _(sample) −{dot over (q)} _(O2)_(—) _(patient)  (1)where {dot over (q)}_(x) is the rate of change (moles/sec) of the molarquantity of x, {dot over (q)}_(x) _(—) _(ambient) is the flow rate intothe chamber, {dot over (q)}_(x) _(—) _(sample) is the flow rate out ofthe chamber, {dot over (q)}_(x) _(—) _(patient) is the patient exchangeflow rate, and {dot over (q)}_(x) _(—) _(chamber) the rate of changewithin the chamber. The symbol N₂ represents nitrogen and all othermetabolically inert atmosphere constituents, like argon.

The ideal gas law states thatPV=qRTwhere P is the pressure (mmHg) of a gas mixture, V is the volume (liter)within which the gas mixture is constrained, q is the moles of gasmolecules, R is the gas constant (62.363 mmHg-liter/mole-° K.), and Tthe temperature (° K.) of the gas. Taking the derivative of both sidesand assuming the rates of change pressure and temperature areinsignificant,P{dot over (V)}={dot over (q)}RTSince the rate of change of volume is the flow rate (liter/sec), F,${PF} = {\overset{.}{q}{RT}}$ and $\overset{.}{q} = {\frac{P}{RT}F}$For a given species x having volumetric fraction f_(x), multiplying bothsides by the fraction yields${\overset{.}{q}}_{x} = {\frac{PF}{RT}f_{x}}$and, in particular, $\begin{matrix}{{{\overset{.}{q}}_{x\_ sample} = {\frac{P_{sample}F_{sample}}{{RT}_{sample}}f_{x\_ sample}}}{{\overset{.}{q}}_{x\_ patient} = \frac{P_{sample}F_{x\_ patient}}{{RT}_{sample}}}{{\overset{.}{q}}_{x\_ ambient} = {\frac{P_{ambient}F_{ambient}}{{RT}_{ambient}}f_{x\_ ambient}}}} & (2)\end{matrix}$The species volumetric fraction in the sample is the same as that in thechamber and given by $\begin{matrix}{f_{x\_ sample} = \frac{q_{x\_ chamber}}{q_{O2\_ chamber} + q_{CO2\_ chamber} + q_{N2\_ chamber}}} & (3)\end{matrix}$and the airflow from ambient into the chamber is driven by the pressuredifference between the two: $\begin{matrix}{F_{ambient} = \frac{P_{ambient} - P_{sample}}{\rho}} & (4)\end{matrix}$where ρ is the inlet orifice resistance to flow in mmHg per liter/secthat is determined experimentally given a chamber, or theoreticallygiven a design.

Equation (4) states a flow sensor is not needed if the flowcharacteristics of the inlet orifice are known. In such a case theF_(ambient) is determined from the ambient and chamber pressuremeasurements.

Equations (1˜4) are sufficient to model and simulate the dynamics of thechamber given

-   -   the ambient humidity-corrected pressure, temperature, species        fractions;    -   inlet flow resistance, chamber volume;    -   the sample flow rate, temperature, and humidity-corrected        pressure; and    -   the patient oxygen and carbon dioxide net consumption and        exhalation flow rates.        The integration of Equations (1) is performed numerically using        any number of methods (e.g. Euler, Newton, Simpson, Huen, etc.).

Since the measurements are in terms of volumetric fractions rather thanmoles, it would be more useful to have the model in these terms. Thisprocess begins by taking the derivative of Equation (3) as$\begin{matrix}{{{\overset{.}{f}}_{x\_ sample} = \frac{\begin{matrix}{{{\overset{.}{q}}_{x\_ chamber}\left( {q_{O2\_ chamber} + q_{CO2\_ chamber} + q_{N2\_ chamber}} \right)} -} \\{q_{x\_ chamber}\left( {{\overset{.}{q}}_{O2\_ chamber} + {\overset{.}{q}}_{CO2\_ chamber} + {\overset{.}{q}}_{N2\_ chamber}} \right)}\end{matrix}}{\left( {q_{O2\_ chamber} + q_{CO2\_ chamber} + q_{N2\_ chamber}} \right)}}{where}} & (5) \\{{{q_{O2\_ chamber} + q_{CO2\_ chamber} + q_{N2\_ chamber}} = \frac{P_{sample}V_{chamber}}{{RT}_{sample}}}{q_{x\_ chamber} = {\frac{P_{sample}V_{chamber}}{{RT}_{sample}}f_{x\_ sample}}}} & (6) \\{f_{N\quad 2} = {1 - f_{O\quad 2} - f_{{CO}\quad 2}}} & (7)\end{matrix}$Substituting Equations (1, 2, 6, 7) into Equation (5) $\begin{matrix}{{{\overset{.}{f}}_{O2\_ sample} = {\frac{1}{V_{chamber}}\left\lbrack {{{- \left( {F_{ambient}^{*} - F_{O2\_ patitent} + F_{CO2\_ patient}} \right)}f_{O2\_ sample}} + {F_{ambient}^{*}f_{O2\_ ambient}} - F_{O2\_ patient}} \right\rbrack}}{{\overset{.}{f}}_{CO2\_ sample} = {\frac{1}{V_{chamber}}\left\lbrack {{{- \left( {F_{ambient}^{*} - F_{O2\_ patient} + F_{CO2\_ patient}} \right)}f_{CO2\_ sample}} + {F_{ambient}^{*}f_{CO2\_ ambient}} + F_{CO2\_ patient}} \right\rbrack}}{{\overset{.}{f}}_{N2\_ sample} = {\frac{1}{V_{chamber}}\left\lbrack {{{- \left( {F_{ambient}^{*} - F_{O2\_ patient} + F_{CO2\_ patient}} \right)}f_{N2\_ sample}} + {F_{ambient}^{*}f_{N2\_ amabient}}} \right\rbrack}}{where}{F_{ambient}^{*} \equiv {\frac{P_{ambient}T_{sample}}{P_{sample}T_{ambient}}F_{ambient}}}} & (8)\end{matrix}$

Equations (8) clearly state the dynamics of the gas fractions are notdirectly dependent on the sample flow, but on the ambient flow. Thismakes sense since the flow of ambient directly changes the gas fractionwhile the sample flow does so only indirectly by lowering the chamberpressure and stimulating ambient flow.

At steady state the derivatives of Equations (8) are zero and, since atsteady stateF _(sample) _(—) _(ss) =F _(ambient) _(—) _(ss) *−F _(O2) _(—)_(patient) _(—) _(ss) +F _(CO2) _(—) _(patient) _(—) _(ss)  (9)then0=−F _(sample) _(—) _(ss) =f _(O2) _(—) _(sample) _(—) _(ss) F_(ambient) _(—) _(ss) *−f _(O2) _(—) _(ambient) −F _(O2) _(—) _(patient)_(—) _(ss)0=−F _(sample) _(—) _(ss) =f _(CO2) _(—) _(sample) _(—) _(ss) F_(ambient) _(—) _(ss) *−f _(CO2) _(—) _(ambient) −F _(CO2) _(—)_(patient) _(—) _(ss)0=−F _(sample) _(—) _(ss) =f _(N2) _(—) _(sample) _(—) _(ss) F_(ambient) _(—) _(ss) *−f _(N2) _(—) _(ambient)These steady equations are solved as $\begin{matrix}{{F_{ambient\_ ss}^{*} = {\frac{1 - f_{{O2\_ sample}{\_ ss}} - f_{{CO2\_ sample}{\_ ss}}}{f_{N2\_ ambient}}F_{sample\_ ss}}}{F_{{O2\_ patient}{\_ ss}} = {\left( {F_{{O2\_ sample}{\_ ss}} - {\frac{1 - f_{{O2} - {sample\_ ss}} - f_{{CO2\_ sample}{\_ ss}}}{f_{N2\_ ambient}}f_{{O2} - {ambient}}}} \right)F_{samplpe\_ ss}}}{F_{{CO2\_ patient}{\_ ss}} = {\left( {f_{{CO2\_ sample}{\_ ss}} - {\frac{1 - f_{{O2\_ sample}{\_ ss}} - f_{{CO2\_ sample}{\_ ss}}}{f_{N2\_ ambient}}f_{CO2\_ ambient}}} \right)F_{sample\_ ss}}}} & (10)\end{matrix}$Variations of these steady state equations are commonly used in steadystate open circuit metabolic devices. The so-called ‘Haldanecorrection’, in which the N₂ fractions come into play, is implicit inthese equations.

It can be shown that the N₂ differential equation has no new informationsince the N₂ correction of Equation (7) is built into the oxygen andcarbon dioxide fraction differentials. The complete dynamic model isthen $\begin{matrix}{{{\overset{.}{f}}_{O2\_ sample} = {\frac{1}{V_{chamber}}\left\lbrack {{{- \left( {F_{ambient}^{*} - F_{O2\_ patient} + F_{CO2\_ pateint}} \right)}f_{O2\_ sample}} + {F_{ambient}^{*}f_{O2\_ ambient}} - F_{O2\_ patient}} \right\rbrack}}{{\overset{.}{f}}_{CO2\_ sample} = {\frac{1}{V_{chamber}}\left\lbrack {{{- \left( {F_{ambient}^{*} - F_{O2\_ patient} + F_{CO2\_ pateint}} \right)}f_{CO2\_ sample}} + {F_{ambient}^{*}f_{CO2\_ ambient}} - F_{CO2\_ patient}} \right\rbrack}}} & (11)\end{matrix}$where the f_(O2) _(—) _(sample), f_(CO2) _(—) _(sample), P_(ambient),P_(sample), T_(ambient), T_(sample), and F_(ambient) are measured, andFO₂ _(—) _(patient) and F_(CO2) _(—) _(patient) are to be determined andare the basis of metabolic parameter determinations. The chamber volume,V_(chamber) is known or estimated by other means as discussed below.

Formation of a Dynamic Metabolic Parameter Analyzer

The general approach to using Equations (11) to dynamically estimate theflows F_(O2) _(—) _(patient) and F_(CO2) _(—) _(patient) is to create anobserver or state estimator. These methods are based on generating aprediction of the unknowns and using these predictions to predict themeasurements. The differences between the predicted and actualmeasurements are used to correct the unknown estimates dynamically witheach new measurement. Say there exist, at time t_(i), an estimate of theflows F_(O2) _(—) _(patient@i/i), and F_(CO2) _(—) _(patient@i/i), amathematical model to predict what they will be at a future time t_(i+1)providing F_(O2) _(—) _(patient@i+1/i) and F_(O2) _(—) _(patient@i+1/i),and a mathematical model to convert them into predictions of theanticipated data at time t_(i+1), providing f_(O2) _(—) _(sample@i+1/i),and f_(CO2) _(—) _(sample@i+1/i). When actual data f_(O2) _(—) _(sample)_(—) _(measured@i+1) and f_(CO2) _(—) _(sample) _(—) _(measured@i+1) areavailable, they are used to correct and update the estimates asF _(O2) _(—) _(patient@i+1/i+1) =F _(O2) _(—) _(patient@i+1/i) +kF_(O2/O2)(f _(O2) _(—) _(sample) _(—) _(measured@i+1) −f _(O2) _(—)_(sample@i+1/i))+kF _(O2/CO2)(f _(CO2) _(—) _(sample) _(—)_(measured@i+1) −f _(CO2) _(—) _(sample@i+1/i))F _(CO2) _(—) _(patient@i+1/i+1) =F _(CO2) _(—) _(patient@i+1/i) +kF_(CO2/O2)(f _(O2) _(—) _(sample) _(—) _(measured@i+1) −f _(O2) _(—)_(sample@i+1/i))+kF _(CO2/CO2)(f _(CO2) _(—) _(sample) _(—)_(measured@i+1) −f _(CO2) _(—) _(sample@i+1/i))f _(O2) _(—) _(patient@i+1/i+1) =f _(O2) _(—) _(patient@i+1/i) +kf_(O2/O2)(f _(O2) _(—) _(sample) _(—) _(measured@i+1) −f _(O2) _(—)_(sample@i+1/i))+kf _(O2/CO2)(f _(CO2) _(—) _(sample) _(—)_(measured@i+1) −f _(CO2) _(—) _(sample@i+1/i))f _(CO2) _(—) _(patient@i+1/i+1) =f _(CO2) _(—) _(patient@i+1/i) +kF_(CO2/O2)(f _(O2) _(—) _(sample) _(—) _(measured@i+1) −f _(O2) _(—)_(sample@i+1/i))+kF _(CO2/CO2)(f _(CO2) _(—) _(sample) _(—)_(measured@i+1) −f _(CO2) _(—) _(sample@i+1/i))  (12)The kf and kF correction gains are determined to make a best correction.

For the purpose of designing an observer or state estimator, theequations representing the dynamic model of the metabolic chamber arelacking a description of the dynamics of the patient flow rates.Appending simple models of the F_(O2) _(—) _(patient) and F_(CO2) _(—)_(patient) dynamics, where their derivatives are considered Gaussianwhite random variables in the manner of stochastic modeling, providesthe complete system model $\begin{matrix}{{{\overset{.}{F}}_{O2\_ patient} = \delta_{{FO2\_ patient}{\_ rate}}}{{\overset{.}{F}}_{CO2\_ patient} = \delta_{{FCO2\_ patient}{\_ rate}}}{{\overset{.}{f}}_{O2\_ sample} = {\frac{1}{V_{chamber}}\left\lbrack {{{- \left( {F_{ambient}^{*} - F_{O2\_ patient} + F_{CO2\_ patient}} \right)}f_{O2\_ sample}} + {F_{ambient}^{*}f_{O2\_ ambient}} - F_{O2\_ patient}} \right\rbrack}}{{\overset{.}{f}}_{CO2\_ sample} = {\frac{1}{V_{chamber}}\left\lbrack {{{- \left( {F_{ambient}^{*} - F_{O2\_ patient} + F_{CO2\_ patient}} \right)}f_{CO2\_ sample}} + {F_{ambient}^{*}f_{CO2\_ ambient}} - F_{CO2\_ patient}} \right\rbrack}}} & (13)\end{matrix}$with measurementsf _(O2) _(—) _(sample) _(—) _(measured) =f _(O2) _(—) _(sample)+δ_(fO2)_(—) _(sample) _(—) _(measurement)f _(CO2) _(—) _(sample) _(—) _(measured) =f _(CO2) _(—)_(sample)+δ_(fCO2) _(—) _(sample) _(—) _(measurement)  (14)where the δ_(F) terms are zero mean uncorrelated Gaussian random statenoise variables having known standard deviations σ_(FO2 patient) _(—)_(rate) and σ_(FCO2) _(—) _(patient) _(—) _(rate), and the δ_(f) termsare Gaussian random measurement noise variables having known standarddeviations σ_(fO2) _(—) _(sample) _(—measurement) and σ_(fCO2) _(—)_(sample) _(—) _(measurement). These equations provide four couplednonlinear time varying stochastic differential equations and are thebasis for the observer state estimator. Defining the estimator statevector, xx _(i) =x(t _(i))=[F _(O2) _(—) _(patiet@i) , f _(O2) _(—) _(sample) , F_(CO2) _(—) _(patient@i) f _(CO2) _(—) _(sample@i)]^(T)the dynamic estimator procedure 100, illustrated in FIG. 2, operates byinitializing 101 the state values; receiving the chamber measurements102; predicting 103 the state at the measurement time; predicting 104the measurements using the predicted state value; correcting 105 thestate value using the measurements and the predicted measurements;outputting 106 the dynamic metabolic parameters derived from thecorrected state values; and waiting 107 for the next set ofmeasurements.

The sample volumetric fraction portions of Equations (13) are equivalentto${\overset{.\quad}{f}}_{O2\_ sample} = {\frac{1}{V_{chamber}}\begin{pmatrix}{{{- F_{{net\_ ambient}/{patient\_ air}}}f_{O2\_ sample}} +} \\F_{{net\_ ambient}/{patient\_ O2}}\end{pmatrix}}$${\overset{.\quad}{f}}_{CO2\_ sample} = {\frac{1}{V_{chamber}}\begin{pmatrix}{{{- F_{{net\_ ambient}/{patient\_ air}}}f_{CO2\_ sample}} +} \\F_{{net\_ ambient}/{patient\_ CO2}}\end{pmatrix}}$F_(net_ambient/patient_O2) = F_(ambient)⁼f_(O2_ambient) − F_(O2_patient)F_(net_ambient/patient_CO2) = F_(ambient)⁼f_(CO2_ambient) − F_(CO2_patient)F_(net_ambient/patient_air) = F_(ambient)⁼ − F_(O2_patientt) + F_(CO2_patient)illustrating their dependence on estimates of the net flow rate of O₂(_(net) _(—) _(ambient/patient) _(—) _(O2)) contributed by ambient andthe patient, net flow rate of CO₂ (F_(net) _(—) _(ambient/patient) _(—)_(CO2)) contributed by ambient and the patient, and the net flow rate(F_(net) _(—) _(ambient/patient) _(—) _(air)) of air into the chambercontributed by ambient and the patient.

If x_(i/i) is the current best estimate of the state based onmeasurements taken up to t_(i), the predicted state at the nextmeasurement time, t_(i+1), is given by integrating the mean values(ignore stochastic terms) of Equations (13) from t_(i) to t_(i+1)beginning with x_(i/i). The integrals are coupled and can be determinedusing numerical integration or by explicit solutions.

Given the predicted state, x_(i+1/i), the measurements are predictedusing the mean values of Equations (14) as the two state vectorelements, f_(O2) _(—) _(sample@i+1/i), and f_(CO2) _(—) _(sample@i+1/i).

Given the predicted measurements, the updated best estimate of the statex_(i+1/i+1) is determined by correcting the predicted state using thepredicted measurements and the actual measurements as in Equations (12).

Systems theory provides various methods for determining the best kf andkF gains ranging from pole placement through Kalman and H-infinityalgorithms. The preferred method is the Kalman filter algorithm as thisis straight forward and based on known measurement noise statisticswhile using the state noise statistics to tune the estimatorperformance. Optimally, the eight kf and kF gains change with each setof measurements according to the Kalman algorithm, but with reasonableassumptions (e.g. the patient O₂ and CO₂ flow rates are small comparedto the ambient flow, the sample volumetric O₂ and CO₂ fractions do notchange greatly from ambient levels, and F*_(ambient) is approximatelyequal to F_(sample)), the Kalman algorithm provides a set of fixed kfand kF.

Useful metabolic parameters include F_(O2) _(—) _(patient) and F_(CO2)_(—) _(patient) as well as the respiratory quotient, metabolic rate, andothers not listed here. Given the patient oxygen and carbon dioxide flowrates, the respiratory quotient (RQ) of the patient is defined as theirratio ${RQ} \equiv \frac{F_{CO2\_ patient}}{F_{O2\_ patient}}$RQ provides evidence of the type of metabolism, fat or lipid. Purecarbohydrate metabolism has a RQ of 1.0, and pure lipid metabolism0.696. Interpolating linearly between these two, the fraction of themetabolism that is carbohydrate (M_(carbohydrate)) and that which islipid (M_(lipid)) are$M_{carbohydrate} = \frac{{RQ} - 0.696}{1.0 - 0.696}$M_(lipid) = 1 − M_(carbohydrate)The resulting estimated patient oxygen and carbon dioxide flow rates arereferenced to the sample temperature and pressure and need to bere-referenced to standard temperature (273.15° K.) and pressure (760mmHg) using$F_{{x\_ patient}{\_ STP}} = {\frac{273.15P_{sample}}{760T_{sample}}F_{x\_ patient}}$and the patient energy expenditure is based (one of manyinterpretations) on the caloric value of the oxygen consumption atstandard conditions: 4.875 kcals/liter of O₂ consumed. The energyexpenditure (EE), also known as the metabolic rate (MR), is then$\begin{matrix}{{EE} = {MR}} \\{= {4.875*F_{{O2\_ patient}{\_ STP}}{{kcal}/{second}}}} \\{= {4.212 \times 10^{5}*F_{{O2\_ patient}{\_ STP}}{{kcal}/{day}}}}\end{matrix}$

FIG. 3 illustrates a dynamic metabolic parameter estimation system 200fitted with an ambient inflow sensor 201 providing inflow rate data 204.The dynamic system 200 uses the dynamic analyzer 202 performingprocedure 100, and produces dynamic metabolic parameters 203 based ondynamic estimates of the patient's oxygen consumption rate 6 and carbondioxide generation rate 5.

FIG. 4 illustrates a conventional metabolic parameter estimation systemretrofitted 300 with a dynamic analyzer 202 and producing dynamicmetabolic parameters 203. A gas outflow sensor 12 and data 15 replacethe gas inflow sensor 201 and data 204. This is possible because, undernormal operational conditions, the inflow (after pressure, temperature,and humidity compensation) and outflow rates are virtually identical(although inflow lags outflow). In this case the F*_(ambient) values ofEquations (13) are replaced by the gas sampling rate F_(sample).

SIMULATION EXAMPLES

-   -   A simulation using Equations (14) is developed of a chamber        initially open to ambient that at time zero is closed and the        sampling process initiated, and for which    -   ambient pressure=760 mmHg    -   ambient and chamber temperatures=22° C.    -   ambient O₂ fraction=0.2095, ambient CO₂ fraction=0.0003, N₂        fraction=0.7902;    -   sample flow rate=20 liter/min;    -   inlet flow resistance=20 mmHg per liter/sec;    -   chamber volume=500 liters;    -   measurement errors σ_(fO2) _(—) _(measurement)=σ_(fCO2) _(—)        _(measurement)=0.00001 (0.001%);    -   flow rate measurement standard deviation σ_(F)=0.1 liter/min;    -   1-second sampling period;        and a patient having    -   respiratory quotient RQ=0.85;    -   initial metabolic rate MR=of 450 kcal/day at STP conditions        changing to 550 kcal/day after 125 minutes        The resulting data is analyzed using Equations (12-14).

FIG. 5 shows the chamber response in its upper row of graphs and theestimated metabolic parameters in the lower row as determined using thesteady state Equations (10) and also by using a fixed kf and kF gaindynamic estimator of Equations (11-12). The correct solution is shown asa dashed line and, whereas the steady state solution (dotted line)converges in roughly 100 minutes, the dynamic estimator (solid line)converges in roughly 10 minutes: a 10:1 improvement. This suggests thepatient can spend 1/10 the time attached to the device. At the sametime, the metabolic parameters are estimated with much less variability.As shown, the dynamic estimator responds quickly to the change inmetabolic rate (energy expenditure) while the steady state method iswaiting for the return of steady state.

-   -   FIG. 6 shows the same simulation but where kf and kF gains        change with each measurement according to the Kalman filter        algorithm. In this case the dynamic estimator converges in        roughly 2 minutes compared to the 100 minutes of the steady        state solution: a 50:1 improvement.

FIG. 7 shows the effect of changing sample flow rates: at 125 minutesthe sample flow rate is abruptly changed to 30 liter/min with no changein metabolic rate. As shown, the kf and kF fixed gain dynamic estimatoris unaffected while the steady state solution is awaiting the return ofsteady state. A dynamic estimator with changing kf and kF gains issimilarly unaffected.

FIG. 8 shows the response of the steady state and fixed kf and kF gaindynamic estimator should the measurement instruments be replaced bythose with ten times more measurement noise. Although the convergenceadvantage (it converges in around 50 minutes) of the dynamic estimatoris reduced to 2:1, it has much less estimate variability than the steadystate.

FIG. 9 shows the less accurate instruments with a variable kf and kFgain dynamic estimator. While its convergence advantage (it converges inaround 10 minutes) is reduced to 10:1, it has much less estimatevariability than the steady state solution.

Although it is possible to low-pass or window-average the valuesgenerated by the steady state method to reduced their variability, thisdoes not eliminate the convergence delay and, since it adds phase,delays convergence even further.

Chamber Volume Determination

An error in V_(chamber) affects the time response of the dynamicestimator and is corrected by sensing the over- or under-shoot of theF_(O2) _(—) _(patient) and F_(CO2) _(—) _(patient) estimates. Too largea V_(chamber) value causes and overshoot, and too small a value causesand undershoot. The effect of a 20% too large value is seen in FIG. 10where the upper trace is for the fixed gain estimator and the lowertrace for the time varying gain estimator. Similarly, FIG. 11 shows theeffect of a 20% too small value of V_(chamber). Compared to FIG. 5 andFIG. 6, these show over- and under-shoots respectively, and the amountof over- or under-shoot is used to adjust the value of V_(chamber).

Other Embodiments

While several illustrative embodiments of the invention have been shownand described, numerous variations and alternate embodiments will occurto those skilled in the art. For example, measurements other thanvolumetric fractions are considered; mathematical models different fromthose presented are considered; any number of other metabolic parametersis considered; estimators other than Kalman filters are considered; andchambers that full or partially enclose a patient, as well as connectonly through a breathing hose, are considered. Such variations andalternate embodiments, as well as others, are contemplated and can bemade without departing from the spirit and scope of the invention asdefined in the appended claims.

1. A metabolism monitor in ambient air having a chamber, a patientconnected to the chamber, a flow of air through the chamber, an airanalyzer providing measurements of chamber air characteristics at aplurality of times, and a metabolic parameter estimator operating on theplurality of measurements comprising: a predictor of metabolicparameters; and a corrector of the predicted metabolic parameters. 2.The monitor of claim 1 where the patient connection comprises having aportion of the patient enclosed by the chamber.
 3. The monitor of claim1 where the characteristics comprise the air flow rate selected from thegroup consisting of the rate entering the chamber and the rate leavingthe chamber.
 4. The monitor of claim 3 where characteristics comprisethe ambient pressure and the chamber air pressure and where the air flowrate entering the chamber is determined by the difference in thesepressures.
 5. The monitor of claim 1 where the predictor comprises: theestimation of the net oxygen flow rate into the chamber as contributedby ambient and the patient; the estimation of the net carbon dioxideflow rate into the chamber as contributed by ambient and the patient;and the estimation of the net air flow rate into the chamber ascontributed by ambient and the patient.
 6. The monitor of claim 1 wherethe characteristics comprise: the volumetric fraction of oxygen and thevolumetric fraction of carbon dioxide and where the predictor comprises:the prediction of the volumetric fraction of oxygen and the predictionof the volumetric fraction of carbon dioxide and the correctorcomprises: a first correction factor dependent on the difference betweenthe measured and predicted oxygen volumetric fractions, and a secondcorrection factor dependent on the difference between the measured andpredicted carbon dioxide volumetric fractions.
 7. The monitor of claim 1further comprises a chamber volume estimator using the measurements. 8.A method to estimate metabolic parameters comprising the steps of:placing a chamber in ambient air; connecting the chamber to a patient;flowing air through the chamber; sampling the chamber air at a pluralityof times and for each sample: measuring characteristics of the gas inthe sample; predicting the metabolic parameters; predicting themeasurements; and correcting the predicted metabolic parameters usingthe predicted measurements and the measurements.
 9. The method of claim8 where the connection is by enclosing a portion of the patient withinthe chamber.
 10. The method of claim 8 where the characteristicscomprise the air flow rate selected from the group consisting of therate into the chamber and the rate out of the chamber.
 11. The method ofclaim 8 where predicting the anticipated metabolic parameters comprisesthe steps of: estimating the net oxygen flow rate into the chamber ascontributed by ambient and the patient; estimating the net carbondioxide flow rate into the chamber as contributed by ambient and thepatient; and estimating the net air flow rate into the chamber ascontributed by ambient and the patient.
 12. The method of claim 8 wherethe characteristics comprise: the volumetric fraction of oxygen and thevolumetric fraction of carbon dioxide and where predicting theanticipated metabolic parameters comprises the steps: predicting thevolumetric fraction of oxygen and predicting the volumetric fraction ofcarbon dioxide and where correcting the predicted metabolic parameterscomprises the steps of: applying a first correction factor dependent onthe difference between the measured and predicted oxygen volumetricfractions, and applying a second correction factor dependent on thedifference between the measured and predicted carbon dioxide volumetricfractions.
 13. A metabolic parameter estimator receiving measurements ofpatient connected chamber air characteristics at a plurality of timescomprising: a predictor of metabolic parameters; and a corrector of thepredicted metabolic parameters.
 14. The estimator of claim 13 where thecharacteristics comprise the chamber air flow rate selected from thegroup consisting of the rate entering the chamber and the rate leavingthe chamber.
 15. The estimator of claim 14 where characteristicscomprise the ambient pressure and the chamber air pressure and where theair flow rate entering the chamber is determined by the difference inthese pressures.
 16. The estimator of claim 13 where the predictorcomprises: the estimation of the net oxygen flow rate into the chamberas contributed by ambient and the patient; the estimation of the netcarbon dioxide flow rate into the chamber as contributed by ambient andthe patient; and the estimation of the net air flow rate into thechamber as contributed by ambient and the patient.
 17. The estimator ofclaim 13 where the characteristics comprise: the volumetric fraction ofoxygen and the volumetric fraction of carbon dioxide and where thepredictor comprises: the prediction of the volumetric fraction of oxygenand the prediction of the volumetric fraction of carbon dioxide and thecorrector comprises: a first correction factor dependent on thedifference between the measured and predicted oxygen volumetricfractions, and a second correction factor dependent on the differencebetween the measured and predicted carbon dioxide volumetric fractions.18. The estimator of claim 13 further comprises a chamber volumeestimator using the measurements.